Efficient operation of modern gas turbine engines require quality control of engine parameters and timely detection of faults, which makes it necessary to continuously develop and complicate the system of regulation, control and diagnostics GTE, using intelligent methods along with the classics.
Currently, the primary processing of information from the sensors, algorithms are applied tolerance control with insufficient efficacy or floating detect gradual failure. To solve this problem, it’s propose to use an intelligent system that implements the method FDI (Fault Detection and Identification), which is based on neural network mathematical model of the engine and neuro-fuzzy classifier. This system can detect and classify abnormal operating conditions of the gas turbine engine, measuring channels and actuators in on-board conditions. A mathematical model of the engine is constructed on the basis of the dynamic neural network – a recurrent multilayer perceptron. For networks training data bench and flight testing of GTE and simulation data obtained with full piecemeal motor model are used. Models designing and debugging produced using a Neural Network Toolbox, which is part of the package Matlab.
Neuro-fuzzy classifier of the engine condition is a fuzzy inference system that is based on neural network. This classifier gives an opinion on serviceability of the engine or its systems on the basis of the error vector obtained by the element-wise comparison of the calculated model data vector Ym with the measured data vector Y. Neuro-fuzzy classifier modeling is made using the ANFIS editor of MATLAB based on the data obtained during flight testing of GTE and GTE failure simulation results and its systems using the full CCD chip unit mathematical model.
During the work were established benefits of using intelligent methods to solve the above problems, such as improving the efficiency of diagnostic floating failures, ease of training and additional training of the models used, etc.